Bibliographic record
Abstract
A look at M. G. Dagenais' contributions (1969, 1973) on threshold regression models and at chapter 9 of S.M. Goldfeld and R.E. Quandt's book (1972) concerning switching regression models suggested to me that a new approach to estimating the threshold model by introducing slack variables might be possible. One of the main advantages of this new method is to simplify to a great extent the estimation of the likelihood function which is reduced partly to the problem of estimating a limited number of simple integrals for each iteration in the process of optimization. In order to facilitate a better understanding of our approach, two main models will be reviewed in the next section: the twin linear probability model (which can be estimated either by OLS, by a combination of probit and OLS, or by the tobit approach) and the threshold model. A critical look at the empirical results obtained by Dagenais (1973) will also be made before closing this section. Our new threshold model with slack variables is presented in section 3 and the main features of our new approach are summarized in the last section of this paper.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.017 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.022 | 0.005 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".